Prediction of Air Quality Indices by Neural Networks and Fuzzy Inference Systems - The Case of Pardubice Microregion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F13%3A39899273" target="_blank" >RIV/00216275:25410/13:39899273 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-642-41013-0_31" target="_blank" >http://dx.doi.org/10.1007/978-3-642-41013-0_31</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-642-41013-0_31" target="_blank" >10.1007/978-3-642-41013-0_31</a>
Alternative languages
Result language
angličtina
Original language name
Prediction of Air Quality Indices by Neural Networks and Fuzzy Inference Systems - The Case of Pardubice Microregion
Original language description
This paper presents a design of models for air quality prediction using feed-forward neural networks of perceptron and Takagi-Sugeno fuzzy inference systems. In addition, the sets of input variables are optimized for each air pollutant prediction by genetic algorithms. Based on data measured by the monitoring station of the Pardubice city, the Czech Republic, models are designed to predict air quality indices for each air pollutant separately and consequently, to predict the common air quality index. Considering the root mean squared error, the results show that the compositions of individual prediction models outperform single predictions of common air quality index. Therefore, these models can be applied to obtain more accurate one day ahead predictions of air quality indices.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/TD010130" target="_blank" >TD010130: Regionalization of economic performance indicators in relation to environmental quality</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Engineering Applications of Neural Networks: 14th International Conference, EANN 2013, Halkidiki, Greece, September 13-16, 2013, Proceedings, Part I
ISBN
978-3-642-41012-3
ISSN
1865-0929
e-ISSN
—
Number of pages
11
Pages from-to
302-312
Publisher name
Springer
Place of publication
Heidelberg
Event location
Halkidiki
Event date
Sep 13, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000345333800031